Quantification Of Synapses Research Articles

Inhibitory control in neuronal networks relies on the extracellular matrix integrity

Inhibitory control is essential for the regulation of neuronal network activity, where excitatory and inhibitory synapses can act synergistically, reciprocally, and antagonistically. Sustained excitation-inhibition (E-I) balance, therefore, relies on the orchestrated adjustment of excitatory and inhibitory synaptic strength. While growing evidence indicates that the brain’s extracellular matrix (ECM) is a crucial regulator of excitatory synapse plasticity, it remains unclear whether and how the ECM contributes to inhibitory control in neuronal networks. Here we studied the simultaneous changes in excitatory and inhibitory connectivity after ECM depletion. We demonstrate that the ECM supports the maintenance of E-I balance by retaining inhibitory connectivity. Quantification of synapses and super-resolution microscopy showed that depletion of the ECM in mature neuronal networks preferentially decreases the density of inhibitory synapses and the size of individual inhibitory postsynaptic scaffolds. The reduction of inhibitory synapse density is partially compensated by the homeostatically increasing synaptic strength via the reduction of presynaptic GABAB receptors, as indicated by patch-clamp measurements and GABAB receptor expression quantifications. However, both spiking and bursting activity in neuronal networks is increased after ECM depletion, as indicated by multi-electrode recordings. With computational modelling, we determined that ECM depletion reduces the inhibitory connectivity to an extent that the inhibitory synapse scaling does not fully compensate for the reduced inhibitory synapse density. Our results indicate that the brain’s ECM preserves the balanced state of neuronal networks by supporting inhibitory control via inhibitory synapse stabilization, which expands the current understanding of brain activity regulation.Graphic abstract

SynQuant: an automatic tool to quantify synapses from microscopy images.

Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Supplementary data are available at Bioinformatics online.

Abstract 116: Single-synapse Analysis Of Synaptic Remodeling In The Post-stroke Rodent Brain

Introduction: The mechanisms of functional recovery after stroke are thought to be based on structural and functional changes in brain circuits adjacent to or connected with the stroke site. Deciphering these changes at the synaptic level is key to understanding the re-organization of the synaptic circuitry (i.e. the connectome). Quantitative information about such synapse rearrangements after stroke has been inadequate however, due to the technical limitations of available methodologies. Here we describe the use of array tomography, a new high-resolution proteomic imaging method, to determine the composition of glutamate and GABA synapses in the post-stroke mouse brain. Methods: A cortical lesion was induced in 12-week-old C57BL/6J male mice using the distal middle cerebral artery occlusion model of ischemia. Small tissue sections were removed from the peri-infarct cortex and ribbons of serial ultrathin (70 nm) sections were obtained using an ultramicrotome. Ribbons were stained with antibodies for the synaptic markers SynapsinI, VGlut1, VGlut2, PSD-95, GAD, VGAT. Analysis of the resultant staining pattern was used to identify subtypes of glutamatergic and GABAergic synapses. Results: At 1 week post-stroke, an increase in GABAergic synapses was observed in layer 5 of the peri-infarct cortex. A sub-analysis of the type of inhibitory interneurons (e.g. parvalbumin, somatostatin) expressing these synapses is pending. In addition, a trend for an increase of VGlut1+2 synapses was also observed. However, there were no detectable differences in total synapse number between stroke-injured and naïve animals, thus suggesting that VGluT2 expression may be upregulated in existing glutamatergic VGluT1 synapses after stroke. Further analysis will be extended to cortical layers 2/3 and 4. Conclusion: These results provide new information about the organization of synaptic circuitry and its plasticity after stroke. Furthermore, it demonstrates how array tomography enables a previously unobtainable level of volumetric visualization and quantification of synapses.

FAST INTERACTIVE QUANTIFICATION OF SYNAPSES IN THE CEREBRAL CORTEX

Synapses are key elements in the organization of neuronal circuits. To analyse these structures, it is now possible to obtain serial images from large samples of nervous tissue in an automated manner through combined focused ion beam milling and scanning electron microscopy. However, the identification, 3D reconstruction and quantification of synapses within these samples are labor intensive procedures that require continuous user intervention. We have developed a software tool to achieve the segmentation of synapses in a reconstructed 3D volume of the cerebral cortex, thereby greatly facilitating and accelerating these processes. The tool is interactive, allowing the user to supervise the segmentation process, to modify the appropriate parameters and to validate the results. Some experimental results obtained with our tool in the rat cerebral cortex are also presented.

Espina: A Tool for the Automated Segmentation and Counting of Synapses in Large Stacks of Electron Microscopy Images

The synapses in the cerebral cortex can be classified into two main types, Gray's type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

A quantitative comparison of synaptic density following perfusion versus immersion fixation in the rat cerebral cortex

The main objective of the present study was to perform an unbiased comparison of immersion vs. perfusion techniques to assess whether we could use the former to quantify synapses through electron microscopy (EM). Using the immersion technique is ideally suited for instances in which the specimen under study could not be perfused under the standard EM protocol. Our results suggest that, despite suboptimal qualitative results, fixation by immersion allows for adequate quantification of synapses.

Ultrastructure and quantification of synapses in the insect nervous system

Standard EM methods can be successfully used to reveal the various organelles of synaptic junctions in different insect species. The individual junctions of a synaptic class exhibit a high level of morphological stereotypy, but the study of serial sections is generally necessary to understand the different appearances of a junction's profiles when it is cut in different planes. Most synaptic profiles seen in single sections may then be attributed to one or a few morphological classes, not to many. Probably most central synapses are of the multiple-contact type, containing a number of postsynaptic elements, with the diversity of the combinations of these providing the major difference between particular synaptic junctions. The different profiles of a synapse when cut serially in oblique, non-canonical section planes provide the investigator with search images, prior knowledge of which is needed for a comprehensive identification of synaptic sites in single sections. The latter can be used to describe the synaptic organization of an unknown neuropile from the variety of synaptic contacts that form between different neurons. This requires that continuity be established between a postsynaptic dendrite and its parent axon, and that the position of the axon can then be used to identify the neuron of origin. Tracing between dendrite and axon can be undertaken either systematically in serial sections of a restricted region or by protracted searches of single sections. The number of synaptic profiles in a single section can be used to estimate the number of synaptic contacts, either in relative terms, as the number of profiles per section in different cells, or as the absolute number of synapses per cell. The latter requires use of correction formulae, taking into account the influence of section thickness and of the mean size of the synaptic junction on the number of synaptic profiles recorded in a particular section.

Quantitative aspects of synapses on Golgi-impregnated neurons.

With the classical Golgi techniques, numerous types of neurons can be distinguished in the cerebral cortex, each with a specific dendritic geometry and pattern of axonal ramifications. In the present review we describe two techniques which allow quantification of synapses on identified neurons: (1) Golgi-rapid impregnation-gold toning-electron microscopy, and (2) Golgi-Kopsch impregnation-gold toning-electron microscopy in combination with staining of the tissue with ethanolic phosphotungstic acid (E-PTA). Both techniques were applied on neurons in the visual cortex of young and adult rabbits. By means of rotating and tilting specimens in the electron microscope, the nondistinctive ultrastructure of obliquely sectioned synapses can be circumvented, leading to precise estimates of asymmetrical vs. symmetrical synapses without complete reconstruction of the neuron.

Quantification of synapses on identified neurons

Quantification of synapses on identified neurons